Predicting Discharge Dates From the NICU Using Progress Note Data

Abstract

Background and Objectives Discharging patients from the Neonatal Intensive Care Unit (NICU) may be delayed for non-medical reasons including the need for medical equipment, parental education, and children’s services. We describe a method to predict and identify patients that will be medically ready for discharge in the next 2–10 days – providing lead-time to address non-medical reasons for delayed discharge. Methods A retrospective study examined 26 features (17 extracted, 9 engineered) from daily progress notes of 4,693 patients (103,206 patient-days) from the NICU of a large, academic children’s hospital. These data were used to develop a supervised machine learning problem to predict days to discharge (DTD). Random forest classifiers were trained using combinations of examined features and ICD-9-based subpopulations to determine the most important features and those features which most accurately predicted days to discharge. Results Three of the four sub-populations (Premature, Cardiac, GI surgery) and all patients combined performed similarly at 2, 4, 7, and 10 DTD with AUC ranging from 0.854–0.865 at 2 DTD and 0.723–0.729 at 10 DTD. Neurosurgery patients performed worse at every DTD measure scoring 0.749 at 2 DTD and 0.614 at 10 DTD. This model was also able to identify important features and provide “rule-of-thumb” criteria for patients close to discharge. Using DTD equal to 4 and 2 features (oral percentage of feedings and weight) we constructed a model with an AUC of 0.843. Conclusion Using clinical features from daily progress notes provides an accurate method to predict when NICU patients are nearing discharge.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

“…11 Recently, increasing emphasis has been placed on the potential of ML techniques to revolutionize medical outcomes research, 12,13 and ML has been used in other areas of medicine to build patient-focused predictive models that can be studied to glean valuable clinical insight. [14][15][16][17][18] We take advantage of ML in an entirely unique way to build a guided ML ensemble that generates predictions that can be used to provide personalized care for meningioma patients. Our approach is distinct in three respects: (1) we use a guided approach to algorithm selection, evaluating and ranking the predictive capabilities of a wide range of ML algorithms; (2) we create an ensemble model, which combines several types of ML algorithms to take advantage of the individual strengths of different model classes; and (3) we train algorithms using holdout datasets and cross-validation, allowing for the inclusion of a large number of predictors without overfitting the final model.…”

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

“…To improve healthcare management and quality, clinical data has already been reused to measure and improve quality [23,24], predict patients length of stay, discharge, readmission, and death [25][26][27][28], and improve infection control [29][30][31]. Data has also been reused for early detection of diseases, pharmacovigilance, and post-market and public health surveillance [32].…”

Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU

“…We previously described a predictive model using a Random Forest to analyze 26 clinical features extracted from the NICU attending physician daily progress note [3]. The goal of that model was to identify patients who would be medically ready for discharge in the next 10, 7, 4, and 2 days with the intent to make clinical staff aware and ready to address in advance the non-medical factors that often delay discharge of patients medically ready to go home.…”

“…Delayed discharge of hospitalized patients, who are medically ready for discharge, is a common occurrence and often related to dependency and the need for post-discharge services [2]. Neonates discharged from the NICU -whether they are premature or recovering from another conditionare prime examples of patients with dependencies on parents and caregivers, who rely heavily on post-discharge services for medical follow-up, home medical equipment, and home nursing [3]. Parents of these fragile infants require a significant amount of training and education regarding the special needs of their newborn, the use of medical equipment, and medication administration.…”